In general, effective integrating the advantages of different trackers can achieve unified performance promotion. In this work, we study the integration of multiple correlation filter (CF) trackers; propose a novel but simple tracking integration method that combines different trackers in filter level. Due to the variety of their correlation filter and features, there is no comparability between different CF tracking results for tracking integration. To tackle this, we propose twofold CF to unify these various response maps so that the results of different tracking algorithms can be compared, so as to boost the tracking performance like ensemble learning. Experiment of two CF methods integration on the data sets OTB demonstrates that the proposed method is effective and promising.

The accurate calculation of the inductance is the most basic problem of the inductor design. In this paper, the core flux density distribution and leakage flux in core window and winding of core-type inductor are analyzed by finite element analysis (FEA) firstly. Based on it, an improved magnetic equivalent circuit with high accuracy flux density distribution (iMEC) is proposed for a single-phase core-type inductor. Depend on the geometric structure, two leakage paths of the core window are modeled. Furthermore, the iMEC divides the magnetomotive force of the winding into the corresponding core branch. It makes the core flux density distribution consistent with the FEA distribution to improve the accuracy of the inductance. In the iMEC, flux density of the core leg has an error less than 5.6% compared to FEA simulation at 150A. The maximum relative error of the inductance is less than 8.5% and the average relative error is less than 6% compared to the physical prototype test data. At the same time, due to the high computational efficiency of iMEC, it is very suitable for the population-based optimization design.